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The Analytics at the Olympics
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August 10, 2016 News

Every athlete at the Rio Olympics, has used every means at their disposal, barring illegal drugs, to improve their performance and become the best they can be. Most have a team of trainers, physicians and staff totally focused on bringing the athletes performance to the next level. However, since the last Olympics in London, there has been an addition to the assistance athletes get that wasn’t there before.

Big data analytics which has in fact been infiltrating every aspect of our lives, will be a strong feature to the performances that will be displayed by the athletes at Rio.

From swimming to cycling and even weight-lifting, almost every sport has turned to big data analytics to find better and more efficient ways to improve.

“Real-time data analytics may not seem like a big leap from an innovation point of view, but it has the potential to enable yet more records to be broken in 2016,” says Dr Helen Meese, head healthcare at the Institution of Mechanical Engineers.

For team Great Britain boxers for instance, using ‘iBoxer’ software, which was developed in conjunction with Sheffield Hallam University, has given them detailed insights into fights and reveals to the fighters possible threats and opportunities to refine their tactics.

Director of Sheffield Hallam’s Advanced Wellbeing Research Centre, Professor Steve Haake says his teams work is increasingly about data-driven performance analysis.

In cycling for instance, focus has been more on finding out how the mechanics are working for the rider rather than on the mechanical aspects of the bicycle.

“Bikes are optimised now – they have bearings and gear sets which are 99% efficient. Most of the work we have done in the last two Olympiads has been interrogating how well these things actually work when you get out there in the field. It’s about data acquisition and complex databases that draw information from lots of places”, he adds.

Data entry as a means to prevent or reduce injury and illness is part of the Australian Institute of Sport (AIS), as the database co-developers’ role, in advising coaches on the best possible training to administer to their athletes. Through their database, smart data analytics and use of wearable sensors, the data is collected and tabulated giving them a clear idea of the areas to focus on.

Nick Brown, deputy director at AIS says “In athlete groups where there is really high engagement with data entry, we have been able to provide coaches with advice on training loads that have seen a reduction in injury and illness,”

There are approximately 2,000 athletes who are closely monitored by AIS and Australia’s National Sporting Organisation each week.

Track cyclists on Team USA are using live data feeds through augmented reality glasses, sensors on components on the bikes to monitor power, speed and revolutions to improve their techniques while they are on the bike.

The glasses which are developed by Solos, will beam the stats via IBM’s cloud platform, to the cyclists, so they can view the key stats without lifting their gaze off the track.

Director at Solos, Ernesto Martinez says “With the ability for the athlete to receive real-time feedback via the Solos smart glasses, they can now adjust on the fly. For example, if Sarah [Hammer] or Kelly [Catlin] need to meet a specific lap time or power metric during an exchange or portion of the race, they will be able to see whether they are hitting the mark or not and adjust accordingly.

“This real-time feedback and adjustment capability will enable faster riding times.”

However, the glasses are not allowed to be used during the actual race.

The question that arises, is it a fair playing field now that richer nations are able to pit their wits against each other’s machines, while poorer nations are left with trying to figure out massive amounts of data through regular trials and errors.

Sporting authorities will need to come up with proper guidelines to mitigate overuse or unfair advantage that might come from big data analytics, or it could tarnish the results of fair play during the games as well as give the big data analytics a bad light.

 

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